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Abstract #4953

A New Multi-Atlas Selection Strategy for Zone Segmentation of the Prostate

Michela Antonelli1, Edward W Johnston 2, Manuel Jorge Cardoso1, Benoit Presles1, Shonit Punwani*2, and Sebastien Ourselin*1,3

1Translational Imaging Group, CMIC, University College London, London, United Kingdom, 2Academic Radiology, University College London Centre for Medical Imaging, London, UK, 3Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK

Automatic segmentation of the prostate into peripheral and transition zones is paramount in developing computer aided diagnosis systems for prostate cancer diagnosis, as cancer behaves differently in each zone. We propose a multi-atlas based segmentation (MAS) algorithm characterized by a new atlas selection strategy: the performance of a subset of atlases is evaluated considering how well that subset segments the image that is most similar to the target image. Comparison of our method with three other MAS algorithms on fifty-five patients shows a statistically significant improvement on the segmentation accuracy.

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